Each year, tens of millions of individuals seek or need the assistance of healthcare professionals and medical facilities. As such, healthcare is one of the largest industries in the country. However, the cost of healthcare is rising at an ever-increasing rate. Controlling and managing the rising cost of healthcare while providing quality medical care is a difficult equilibrium. Medical providers and facilities often struggle to provide quality care when the medical providers and facilities are not getting paid due to the increase in healthcare and medical expenses. As such, medical providers and medical facilities often struggle to predict the best allocation of resources as well as forecasting revenue, in order to provide quality care.
An ability to eliminate or reduce wasteful procedures while lowering overpriced services are often suggested to help reduce healthcare and medical expenses. Actuarial models of cost prediction, especially for healthcare, have typically not been as accurate or as helpful as desirable. Traditional actuarial methods of predicting medical costs are based on an economic model using standard demographic data (such as age and sex), and do not take actual real time data. Similarly, predictive modeling of medical resources based on actual use is not as helpful due to the numerous parts of in a hospital setting.